System and method for enhancing functional medical images

ABSTRACT

Systems and methods for generating a medical image of a subject that includes functional information. First, two medical images are acquired. One is weighted based on functional information reflecting physiological functions of the subject and the other weighted based on anatomic information of the subject. A difference image between the two images are generated. By subjecting the difference image and the second image to a localized kernel, a local similarity image is generated. Using the local similarity image, an improved difference image is generated. Lastly, by subtracting the improved difference image from the first image, an enhanced medical image that retains the functional information reflecting physiological functions of the subject is generated.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of pending PCT InternationalApplication No. PCT/US2016/029976 filed Apr. 29, 2016 and is based on,claims priority to, and incorporates herein by reference, U.S.application Ser. No. 14/714,015, filed May 15, 2015, and entitled“System and Method for Enhancing Functional Medical Images.”

STATEMENT REGARDING FEDERALLY-SPONSORED RESEARCH

This invention was made with government support under 2 R01MH080729-04A1 awarded by the National Institutes of Health. Thegovernment has certain rights in the invention.

BACKGROUND

Frequently in medical imaging, a functional image with the contrast ofinterest has a signal-to-noise ratio (SNR) or spatial resolution lowerthan ideal. For example, arterial spin labeling (ASL) is a technique inmagnetic resonance imaging (MRI) that can be used to provide functionalinformation using MRI, despite the fact that MRI excels at anatomicimaging and does not inherently include functional information inanatomic images. Unfortunately, using ASL, like many other techniques toacquire functional information using MRI, yields a noisy, lowerresolution image than may be achieved with images that are intended tobe purely anatomic, such as T1-weighted or T2-weighted anatomic images.

As a result, many have sought to improve the quality of functionalimages acquired using MRI, such as by increasing SNR or compensating forlow resolutions. One way to improve the quality of a functional image isto combine the functional image with the anatomic image. In thesemethods, the acquired functional images are combined with anatomicimages in an effort to improve SNR or resolution. These fusion methodsare designed to fuse the information in both the anatomic and functionalimages to maximize the information in the resulting fusion image. Theresulting image would include extra structures that may help localize alesion. But, importantly, this also results in a fusion image of adifferent contrast. So these “fusion” methods generally produce acombined image with information not readily attributable to either thefunctional image or the anatomic image, or provide a confusing attemptto overlay both sets of information. As such, the functional informationcan be obscured or, worse, misleading. Thus, such “fusion” orenhancement methods are often shunned in the clinical environmentbecause clinicians cannot allow the functional data to be obscured orrendered inaccurate.

It would be desirable to have a system and method for enhancing afunctional images without interfering or degrading the functionalinformation.

SUMMARY

The present disclosure overcomes the aforementioned drawbacks byproviding systems and methods for enhancing functional medical images.To preserve the contrast from the functional image and also include theanatomic information from the anatomic image, an improved differenceimage is subtracted from the functional image to partially remove localsimilarity.

In accordance with one aspect of the disclosure, a method for generatinga medical image of a subject including functional information isprovided. First, two medical images are acquired. One is weighted basedon functional information reflecting physiological functions of thesubject and the other is weighted based on anatomic information of thesubject. A difference image between the two images is generated. Bysubjecting the difference image and the second image to a localizedkernel, a local similarity image is generated. Using the localsimilarity image, an improved difference image is generated. Lastly, bysubtracting the improved difference image from the first medical image,an enhanced medical image that retains the functional information isgenerated.

In accordance with another aspect of the disclosure, a magneticresonance imaging (MRI) system is disclosed that includes a magnetsystem configured to generate a polarizing magnetic field about at leasta portion of a subject arranged in the MRI system and a magneticgradient system including a plurality of magnetic gradient coilsconfigured to apply at least one magnetic gradient field to thepolarizing magnetic field. The MRI system also includes a radiofrequency (RF) system configured to apply an RF field to the subject andto receive magnetic resonance (MR) signals therefrom and a computersystem. The computer system is programmed to first acquire two MRIimages. One image is weighted based on functional information reflectingphysiological functions of the subject, and the other is weighted basedon anatomic information of the subject. The computer system is alsoprogrammed to generate a difference image between the two images, thengenerate a local similarity image by subjecting the difference image andthe second image to a localized kernel. The computer system is furtherprogrammed to generate an improved difference image using the localsimilarity image. Lastly, the computer system is programmed to generatean enhanced medical image by subtracting the improved difference imagefrom the first medical image. The enhanced image retains the functionalinformation.

The foregoing and other aspects and advantages of the invention willappear from the following description. In the description, reference ismade to the accompanying drawings, which form a part hereof, and inwhich there is shown by way of illustration a preferred embodiment ofthe invention. Such embodiment does not necessarily represent the fullscope of the invention, however, and reference is made therefore to theclaims and herein for interpreting the scope of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an example of a magnetic resonance imaging(MRI) system configured to employ the present disclosure.

FIG. 2 is a flow chart setting forth the steps of one non-limitingexample of a method for generating an enhanced medical image implementedaccording to the present application.

FIG. 3 is a flow chart setting forth an example method for generating anenhanced brain image implemented according to the present application.

FIG. 4A shows the error of the fusion image in relation with kernelsizes.

FIG. 4B shows structure similarity of the fusion image with a simulatedideal functional image in relation with kernel sizes.

FIG. 5A shows the error of the fusion image in relation with therelaxation factor.

FIG. 5B shows the speed of convergence of the systems and methods asdisclosed herein in relation with the relaxation factor.

DETAILED DESCRIPTION

The systems and methods disclosed herein can be used to enhance afunctional medical image with localized anatomic information. Themedical images can be acquired with a magnetic resonance imaging (MRI)system, such as the below-described system 100.

Referring particularly to FIG. 1, an example of an MRI system 100 isillustrated. The MRI system 100 includes a workstation 102 having adisplay 104 and a keyboard 106. The workstation 102 includes a processor108 that is commercially available to run a commercially-availableoperating system. The workstation 102 provides the operator interfacethat enables scan prescriptions to be entered into the MRI system 100.The workstation 102 is coupled to four servers: a pulse sequence server110; a data acquisition server 112; a data processing server 114; and adata store server 116. The workstation 102 and each server 110, 112,114, and 116 are connected to communicate with each other.

The pulse sequence server 110 functions in response to instructionsdownloaded from the workstation 102 to operate a gradient system 118 anda radiofrequency (RF) system 120. Gradient waveforms necessary toperform the prescribed scan are produced and applied to the gradientsystem 118, which excites gradient coils in an assembly 122 to producethe magnetic field gradients G_(x), G_(y), and G_(z) used for positionencoding MR signals. The gradient coil assembly 122 forms part of amagnet assembly 124 that includes a polarizing magnet 126 and awhole-body RF coil 128 (or a head (and neck) RF coil for brain imaging).

RF excitation waveforms are applied to the RF coil 128, or a separatelocal coil, such as a head coil, by the RF system 120 to perform theprescribed magnetic resonance pulse sequence. Responsive MR signalsdetected by the RF coil 128, or a separate local coil, are received bythe RF system 120, amplified, demodulated, filtered, and digitized underdirection of commands produced by the pulse sequence server 110. The RFsystem 120 includes an RF transmitter for producing a wide variety of RFpulses used in MR pulse sequences. The RF transmitter is responsive tothe scan prescription and direction from the pulse sequence server 110to produce RF pulses of the desired frequency, phase, and pulseamplitude waveform. The generated RF pulses may be applied to the wholebody RF coil 128 or to one or more local coils or coil arrays.

The RF system 120 also includes one or more RF receiver channels. EachRF receiver channel includes an RF preamplifier that amplifies the MRsignal received by the coil 128 to which it is connected, and a detectorthat detects and digitizes the quadrature components of the received MRsignal. The magnitude of the received MR signal may thus be determinedat any sampled point by the square root of the sum of the squares of theI and Q components:M=√{square root over (I ² +Q ²)}  (1),and the phase of the received MR signal may also be determined:

$\begin{matrix}{\varphi = {{\tan^{- 1}\left( \frac{Q}{I} \right)}.}} & (2)\end{matrix}$

The pulse sequence server 110 also optionally receives patient data froma physiological acquisition controller 130. The controller 130 receivessignals from a number of different sensors connected to the patient,such as electrocardiograph (ECG) signals from electrodes, or respiratorysignals from a bellows or other respiratory monitoring device. Suchsignals are typically used by the pulse sequence server 110 tosynchronize, or “gate,” the performance of the scan with the subject'sheart beat or respiration.

The pulse sequence server 110 also connects to a scan room interfacecircuit 132 that receives signals from various sensors associated withthe condition of the patient and the magnet system. It is also throughthe scan room interface circuit 132 that a patient positioning system134 receives commands to move the patient to desired positions duringthe scan.

The digitized MR signal samples produced by the RF system 120 arereceived by the data acquisition server 112. The data acquisition server112 operates in response to instructions downloaded from the workstation102 to receive the real-time MR data and provide buffer storage, suchthat no data is lost by data overrun. In some scans, the dataacquisition server 112 does little more than pass the acquired MR datato the data processor server 114. However, in scans that requireinformation derived from acquired MR data to control the furtherperformance of the scan, the data acquisition server 112 is programmedto produce such information and convey it to the pulse sequence server110. For example, during prescans, MR data is acquired and used tocalibrate the pulse sequence performed by the pulse sequence server 110.Also, navigator signals may be acquired during a scan and used to adjustthe operating parameters of the RF system 120 or the gradient system118, or to control the view order in which k-space is sampled. In allthese examples, the data acquisition server 112 acquires MR data andprocesses it in real-time to produce information that is used to controlthe scan.

The data processing server 114 receives MR data from the dataacquisition server 112 and processes it in accordance with instructionsdownloaded from the workstation 102. Such processing may include, forexample: Fourier transformation of raw k-space MR data to produce two orthree-dimensional images; the application of filters to a reconstructedimage; the performance of a backprojection image reconstruction ofacquired MR data; the generation of functional MR images; and thecalculation of motion or flow images.

Images reconstructed by the data processing server 114 are conveyed backto the workstation 102 where they are stored. Real-time images arestored in a data base memory cache (not shown), from which they may beoutput to operator display 104 or a display 136 that is located near themagnet assembly 124 for use by attending physicians. Batch mode imagesor selected real time images are stored in a host database on discstorage 138. When such images have been reconstructed and transferred tostorage, the data processing server 114 notifies the data store server116 on the workstation 102. The workstation 102 may be used by anoperator to archive the images, produce films, or send the images via anetwork or communication system 140 to other facilities that may includeother networked workstations 142.

The communication system 140 and networked workstation 142 may representany of the variety of local and remote computer systems that may beincluded within a given clinical or research facility including thesystem 100 or other, remote location that can communicate with thesystem 100. In this regard, the networked workstation 142 may befunctionally and capably similar or equivalent to the operatorworkstation 102, despite being located remotely and communicating overthe communication system 140. As such, the networked workstation 142 mayhave a display 144 and a keyboard 146. The networked workstation 142includes a processor 148 that is commercially available to run acommercially-available operating system. The networked workstation 142may be able to provide the operator interface that enables scanprescriptions to be entered into the MRI system 100.

Referring to FIG. 2, a flowchart 200 of an example method implementedaccording to the present application is provided. In step 202, twomedical images are acquired. One depicts physiological functions of asubject while the other depicts anatomy of the subject. An example of afunctional image can be a perfusion image acquired with arterial spinlabeling pulse sequence or a P+ image, and an example of anatomic imagecan be a T1-weighted image, or a T2-weighted image, or afluid-attenuated image acquired with a fluid-attenuated inversionrecovery (FLAIR) pulse sequence of the same location in the subject. Theimages can be acquired with different medical modalities. For example,the functional image can be acquired by a positron emission tomography(PET) or a single-photon emission computed tomography (SPECT), while theanatomic image can be acquired by MRI.

The two images can be denoted as I and T, with I for the functionalimage and T for the anatomic image. The anatomic image may have betterimage quality, resolution, SNR, contrast, or other such desirableattributes than the functional image. The functional image can be viewedas comprising two images: one as similar as possible to the anatomicimage, and the other as dissimilar as possible to the anatomic image. Ametric of similarity and dissimilarity can be the mutual information ininformation theory.

Still referring to FIG. 2, in step 204, a difference image between thetwo images may be generated. When generating the difference image, itmay be desirable to threshold or otherwise restrict the image to a spaceof interest. For example, both images is normalized as:

$\begin{matrix}{{I_{nml} = \frac{I - \left\langle I \right\rangle}{\left( {\left\langle I^{2} \right\rangle - \left\langle I \right\rangle^{2}} \right)}},} & (3) \\{and} & \; \\{{T_{nml} = \frac{T - \left\langle T \right\rangle}{\left( {\left\langle T^{2} \right\rangle - \left\langle T \right\rangle^{2}} \right)}},} & (4)\end{matrix}$where I_(nml) and T_(nml) are the normalized functional and anatomicimages, respectively; and

·

denotes expectation (i.e., average across image voxels).

A similarity image S between I and T can be calculated as:

$\begin{matrix}{S = {\frac{\left\langle {I_{nml},T_{nml}} \right\rangle}{\left\langle {T_{nml},T_{nml}} \right\rangle}{T_{nml}.}}} & (5)\end{matrix}$Then the difference image D between I and T is:D=I _(nml) −S  (6).As a result, two images are created: D—an orthogonal image to T as theexpectation of DT is zero, and S—an image that is as similar as possibleto the anatomic image T.

As indicated generally at 206, an improved difference image may becreated. The similarity can be improved to be a more subtle andregionally specific, for example by iteratively removing similaritiesfrom D on a regional basis.

This process can be part of an iterative process. Consider a localizedkernel, K, with unit normalization. In particular, at step 208, a localsimilarity can be calculated using the localized kernel K, such as:

$\begin{matrix}{{S^{n} = {\frac{\left\langle {{KD}_{imp}^{n - 1},{KT}_{nml}} \right\rangle}{\left\langle {{KT}_{nml},{KT}_{nml}} \right\rangle}{KT}_{nml}}},} & (6)\end{matrix}$where n=1, 2, . . . denotes the nth iteration, S^(n) denotes the localsimilarity calculated at the nth iteration, and D_(imp) ^(n−1) denotesthe improved difference image calculated in the prior iteration. Theinitial improved difference image D_(imp) can be the difference imagecalculated according to Eqs. (5) and (6), i.e., D_(imp) ⁰=D. In oneconfiguration, the initial improved difference image D_(imp) can beI_(nml)—i.e., D_(imp) ⁰=I_(nml). The convergence of the iterativeprocess may be slower with this initialization than that initializedwith a difference image.

The local similarity can be further improved by removing components thatare not orthogonal to the anatomic image T. With the local similaritycalculated, at step 210, the difference image can be improved byremoving the local similarity S^(n) as:D _(imp) ^(n) =D _(imp) ^(n−1) −εS ^(n)  (7),where ε is a relaxation factor. The relaxation factor can be a positivenumber and kept significantly less than one so that the process canslowly and uniformly progress to an image that is as different aspossible from the anatomic image. The kernel K can be moved around inthe image either systematically or randomly, and also be expanded orcontracted while moving around. The kernel can be a box or cubic kernel,a smooth function, or a wavelet. An example smooth function can be aGaussian kernel that can be used to reduce ringing artifacts. A Gaussiankernel can be expressed as:

$\begin{matrix}{K = {\exp\left( {- \frac{\left( {r - r_{0}} \right)^{2}}{2\sigma^{2}}} \right)}} & (7)\end{matrix}$The position of a Gaussian kernel depends on r₀ and the kernel sizedepends on σ. The kernels can be normalized as:

$\begin{matrix}{K = \frac{K}{\sum K}} & (7)\end{matrix}$

At decision block 212, the iterative process can stop when apredetermined criterion is met. For example, the predetermined criterionmay include a maximum iteration number (e.g., when n reaches 200).Alternatively, the predetermined criterion may relate to the qualitativeimprovement. Even when tied to a qualitative measure, a quantitativechange threshold may be used. The predetermined criterion may be theroot mean square (rms) across the image having a change between D_(imp)^(n−1) and D_(imp) ^(n) of less than a particular value. For example,the change may be less than 0.1% of the rms of the prior estimateD_(imp) ^(n−1).

Also, more than one anatomic image can be used in the systems andmethods as disclosed herein. The multiple anatomic images can havedifferent contrasts. The local similarity in Eq. (6) can be searchedwith each anatomic image T_(nml,i) as:

$\begin{matrix}{{S_{i}^{n} = {\frac{\left\langle {{KD}_{{imp},i}^{n - 1},{KT}_{{nml},i}} \right\rangle}{\left\langle {{KT}_{{nml},i},{KT}_{{nml},i}} \right\rangle}{KT}_{{nml},i}}},} & (7)\end{matrix}$and the difference image can be improved with each anatomic image as:D _(imp,i) ^(n) =D _(imp,i) ^(n−1) −εS _(i) ^(n)  (7).

When two or more different contrast anatomic images are used, the fusionimage can be generated by using an anatomic image of one contrast firstand switching to an anatomic image of a different contrast when theiteration improvement is less than a certain threshold, such as 1%. Thecontrast of the fused image is dominated by that of the startinganatomic image although there is a small change in the fused image afterswitching to a different anatomic image.

Moving the kernel around during the iteration process can becomputationally intense. Alternatively, the kernel calculation can beperformed simultaneously across all kernel positions using the Fouriertransform convolution theorem. For example, the localized similarity canbe calculated as:

$\begin{matrix}{S^{n} = {\left( {\left( \frac{\left\langle {K*D_{imp}^{n - 1}T_{nml}} \right\rangle}{\left\langle {{KK}*D_{imp}^{n - 1}T_{nml}} \right\rangle} \right)*K} \right){T_{nml}.}}} & (8)\end{matrix}$

A kernel having about twice the width of the point spread function ofthe functional image can be used. A regularization term can be added inthe denominator of the above Eq. (8) to prevent the calculation fromblowing up near the edges of the image where the signals are zero orclose to zero.

In one configuration, a functional image can be enhanced by combiningthe functional image and the anatomic image as a normalizedmultiplication. For example:

$\begin{matrix}{I_{imp} = {\left( \frac{K*I}{K*T} \right){T.}}} & (9)\end{matrix}$This produces similar results to the iterative process described abovewhen a small kernel is used.

Once the iterative process is stopped, at step 214, a fused image thatpreserves the functional information of the functional image butenhances the overall image quality with information from the anatomicimages, can be generated as:I _(imp) =I _(nml) −D _(imp,final)  (10).

Referring now to FIG. 3, a flowchart of an example implementation of thesystems and methods as disclosed herein to enhance an ASL image isprovided. First, an anatomic image 302 and a functional image 304, suchas an ASL image, are acquired. The ASL image 304 depicts perfusion of asubject's brain. Its SNR and resolution are poor compared to theanatomic image 302 such that its diagnostic value is limited. Next, aglobal dissimilarity image 306 is generated and used to initialize aniterative process to improve the dissimilarity image 306 byincorporating a local similarity image 308. The local similarity image308 is generated using a localized kernel 314. The local similarity inrelation with the localized kernel 314 is preserved and included intothe improved dissimilarity image 306 (see the localized kernel 314 inthe anatomic image 302, the localized similarity image 308, and thedissimilarity image 306). The dissimilarity image 308 may be used toupdate the localized similarity image 308 if the criterion 310 is notmet. This iterative process is repeated until the criterion 310 is met.Once the criterion is met, a fusion image 312 is generated using the ASLimage 304 and the final dissimilarity image 306. The fusion image 312preserves the contrast in the original ASL image 304 but with improvedimage quality and, thus, a better diagnostic value than the original ASLimage 304.

Numerical simulation can be used to select parameters, such as thekernel size and the relaxation factor. “Ideal” 3D ASL images can benumerically generated as follows. First, T1-weighted images of highresolution and high SN are acquired. The images can be directly acquiredby scanning a subject with a T1-weighted pulse sequence on an MRI systemor transferred from a storage unit, such as a computer, database, ahand-held device, or the cloud. These T1-weighted images are used as theanatomic images.

Then, the T1-weighted images may be segmented into gray matter (GM),white matter (WM), cerebrospinal fluid, bone, and soft tissue. Toachieve a contrast similar to an ASL MRI scan, the perfusion values ofthe GM and WM may be assigned with values similar to their clinicalvalues. For example, GM of the segmented images may be given perfusionvalue of 70 ml/100 g/min, and a value of 20 ml/100 g/min is assigned toWM. All regions other than GM and WM are removed to construct anumerical phantom. The phantom is further smoothed by a Gaussianconvolution kernel (e.g., size 3×3×3 pixels and standard deviation 0.65pixels) to avoid unrealistic transition. As such, the images of thephantom have a perfusion contrast but with high resolution. They arereferred to as ideal ASL images.

To simulate the image quality of an ASL scan, the ideal ASL images aretransformed into k-space, filtered with a low-pass-filter, and addedwith complex white Gaussian noise. This results in a low quality ASLimage, e.g. with low SNR.

To optimize kernel sizes, the fusion image can be calculated withvarying kernel sizes σ of a Gaussian kernel from 1 to 7 pixels.Referring to FIGS. 4A and 4B, the performance of the method as disclosedherein with different kernel sizes is provided. The minimum root meansquare error (RMSE) compared with the ideal ASL images is achieved withkernel of 3.4 pixels (FIG. 4A) and maximum structural similarity (SSIM)is achieved with kernel of 3 pixels (FIG. 4B).

A fused image acquired with the systems and methods as disclosed hereincan maintain the contrast of the initial ASL image and, at the sametime, includes high resolution anatomic information from the anatomicimage. Thus, a better approximation of the ideal ASL image is generated.Fused images generated with small kernel sizes detect detail structuresand follow the contrast of the initial ASL image better than those withlarge kernel sizes. But, on the other hand, they have limitedperformance on noise suppression and more Gibbs ringing artifacts.

In the systems and methods as disclosed herein, the relaxation factor Econtrols the speed of convergence of the iterative process. To optimizethe relaxation factor, the performance of the systems and methods can beevaluated with relaxation factors from 0.1 to 0.5.

Referring to FIGS. 5A and 5B, the performance of the systems and methodsas disclosed herein with different relaxation factor ϵ is provided.Using an intermediate relaxation factor (around 0.2-0.3) slightlyimproves the image quality (see the decreased errors for intermediaterelaxation factors shown in FIG. 5A), and using a small relaxationfactor increases computational time significantly (FIG. 5B).

The systems and methods as disclosed herein can be used to preserve thecontrast and information in the functional image and, at the same time,improve the quality of the functional image with detailed structuralinformation from the anatomic image for diagnostic purposes. Compared toother fusion methods, the error (RMSE) of the fusion image generatedwith the systems and methods as disclosed herein is reduced andstructural similarity (SSIM) is improved. The systems and methods asdisclosed herein can also be used to suppress noise and increasecontrast-to-noise (CNR) ratio, as shown in the table below. Moreover,the systems and methods as disclosed herein are less computationallycomplex than, for example, a wavelet method because the wavelet methodrequires tradeoff coefficients between functional and anatomic images.

CNR Lesion1 Lesion2 Original image 28.4 62.7 Enhanced with a waveletmethod 20.8 53.7 Enhanced with the systems and 43.5 97.7 methods asdisclosed herein

If the contrast in the anatomic image is close to that of the functionalimage, the quality of the image produced with the systems and methodsdisclosed herein can be further enhanced. To be in such desiredcontrast, the anatomic image can be preprocessed with a prioriinformation. For example, with some a priori information, a T1-weightedimage can be processed with a Gaussian transformation so that graymatter is the brightest in the image.

If the subject moves during the medical image acquisition or a differentmedical modality is used to acquire the functional image from that forthe anatomic image, the anatomic and functional images may not depictthe same location in the subjection. The two images can be spatiallyregistered before the systems and methods are applied to fuse them. Aperson skilled in the art would appreciate that any available imageregistration methods can be used to register the two images.

The systems and methods as disclosed herein can be applied to imageswithout fine-tuning parameters. For example, as shown above, the systemsand methods can be applied without choosing parameters except the kernelsize.

The systems and methods as disclosed herein can be used to preserve thelow frequency information in the functional image. The systems andmethods can use multiplicative combinations (e.g., as shown in Eqs. (8)or (9)), instead of a prior method using addition of components, andprovide better results than the prior method.

The systems and methods can be integrated with an imaging system or aviewer, or implemented as a separate module where data can be acquiredfrom another system, such as an imaging system, a database, a network, acomputer, a hand-held device, or the cloud. It can be tied to aparticular application, such as ASL using MRI, or be applied to enhancea functional medical image of different contrast mechanisms or otherimaging modalities.

The present invention has been described in terms of one or morepreferred embodiments, and it should be appreciated that manyequivalents, alternatives, variations, and modifications, aside fromthose expressly stated, are possible and within the scope of theinvention.

As used in the claims, the phrase “at least one of A, B, and C” means atleast one of A, at least one of B, and/or at least one of C, or any oneof A, B, or C or combination of A, B, or C. A, B, and C are elements ofa list, and A, B, and C may be anything contained in the Specification.

What is claimed is:
 1. A method for generating a medical image of asubject including functional information, the steps of the methodcomprising: a) acquiring medical image data including a first medicalimage data weighted based on functional information reflectingphysiological functions of the subject and a second medical image dataweighted based on anatomic information of the subject; b) generatingdifference image data between the first medical image data and thesecond medical image data; c) generating similarity image data bysubjecting at least the difference image data to a kernel; d) generatingimproved difference image data using the similarity image data; and e)generating an enhanced medical image using the improved difference imagedata the medical image data to retain the functional informationreflecting physiological functions of the subject.
 2. The method asrecited in claim 1, wherein step b) includes normalizing the medicalimage data as: $\begin{matrix}{{I_{nml} = \frac{I - \left\langle I \right\rangle}{\left( {\left\langle I^{2} \right\rangle - \left\langle I \right\rangle^{2}} \right)}},} \\{and} \\{{T_{nml} = \frac{T - \left\langle T \right\rangle}{\left( {\left\langle T^{2} \right\rangle - \left\langle T \right\rangle^{2}} \right)}},}\end{matrix}$ where I_(nml) and T_(nml) denote the normalized first andsecond images respectively, I and T denote the first medical image dataand the second medical image data respectively, and

·

denotes expectation; and generating the similarity image data and thedifference image data as:$S = {\frac{\left\langle {I_{nml},T_{nml}} \right\rangle}{\left\langle {T_{nml},T_{nml}} \right\rangle}T_{nml}}$andD=I _(nml) −S, where D and S denote the difference image data and thesimilarity image data, respectively.
 3. The method as recited in claim1, further comprising repeating step d) until a predetermined criterionis met, wherein the predetermined criterion includes one of a maximum ofiterations or a change threshold between iterations.
 4. The method asrecited in claim 3, wherein the similarity image data in step c) iscalculated as:${S^{n} = {\frac{\left\langle {{KD}_{imp}^{n - 1},{KT}_{nml}} \right\rangle}{\left\langle {{KT}_{nml},{KT}_{nml}} \right\rangle}{KT}_{nml}}},$where S^(n) denotes the similarity image data, K denotes the kernel,T_(nml) is normalized second image data, and D_(imp) ^(n−1) is theimproved difference image data from a prior iteration.
 5. The method asrecited in claim 3, wherein the similarity image data in step c) iscalculated as:${S^{n} = {\left( {\left( \frac{\left\langle {K*D_{imp}^{n - 1}T_{nml}} \right\rangle}{\left\langle {{KK}*D_{imp}^{n - 1}T_{nml}} \right\rangle} \right)*K} \right)T_{nml}}},$where S^(n) denotes the similarity image data, K denotes the kernel, *denotes convolution, T_(nml) is normalized second image data, andD_(imp) ^(n−1) is the improved difference image data from a prioriteration.
 6. The method as recited in claim 1, wherein a position ofthe kernel is randomly selected.
 7. The method as recited in claim 1,wherein the kernel is a box kernel, a cubic kernel, a Gaussian kernel,or a wavelet.
 8. The method as recited in claim 1, wherein the firstmedical image data and the second medical image data include magneticresonance image data.
 9. The method as recited in claim 1, wherein stepd) includes generating the improved difference image data by subtractinga product of the similarity image data and a user-selected relaxationfactor.
 10. The method as recited in claim 1, wherein step d) includesgenerating an image orthogonal to an image reconstructed from the secondimage data and an image that is similar to the image reconstructed fromthe second image data.
 11. A method for generating a medical image of asubject including functional information, the steps of the methodcomprising: a) acquiring a first medical image dataset weighted based onfunctional information reflecting physiological functions of the subjectand a second medical image dataset weighted based on anatomicinformation of the subject; b) generating an improved dataset convolvinga kernel with one of the first medical image dataset and the secondmedical image dataset; and c) generating a fused image using theimproved dataset and the second medical image dataset, wherein the fusedimage retains the functional information reflecting physiologicalfunctions of the subject and the anatomic information of the subject.12. The method as recited in claim 11, wherein generating the improveddataset includes generating a fraction as a kernel convolved with thefirst medical image dataset divided by the kernel convolved with thesecond medical image dataset and generating the fused image includesmultiplying the fraction with the second medical image dataset.
 13. Themethod as recited in claim 11, further comprising: generating theimproved dataset as a first kernel convolved with the first medicalimage dataset divided by a second kernel convolved with the secondmedical image dataset, wherein the first kernel and the second kernelare not identical in size and shape.
 14. The method as recited in claim11, wherein the kernel is a box kernel, a cubic kernel, a Gaussiankernel, or a wavelet.
 15. The method as recited in claim 11, wherein thefunctional information shows contrast of arterial spin labeling.
 16. Themethod as recited in claim 11 wherein step b) includes: normalizing thefirst medical image dataset and the second medical image dataset togenerate a normalized first image dataset and a normalized second imagedataset as: $\begin{matrix}{{I_{nml} = \frac{I - \left\langle I \right\rangle}{\left( {\left\langle I^{2} \right\rangle - \left\langle I \right\rangle^{2}} \right)}},} \\{and} \\{{T_{nml} = \frac{T - \left\langle T \right\rangle}{\left( {\left\langle T^{2} \right\rangle - \left\langle T \right\rangle^{2}} \right)}},}\end{matrix}$ where I_(nml) and T_(nml) denote the normalized first andsecond images respectively, I and T denote the first medical imagedataset and the second medical image dataset respectively, and

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denotes expectation; and generating a difference image dataset betweenthe first medical image dataset and the second medical image dataset;generating a similarity image dataset by subjecting the difference imagedataset and the second medical image dataset to the kernel.
 17. Themethod as recited in claim 11, wherein the kernel is a box kernel, acubic kernel, a Gaussian kernel, or a wavelet.